AIMC Topic: Humans

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Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

Scientific reports
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct da...

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

NPJ systems biology and applications
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Com...

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

JMIR mHealth and uHealth
The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT pose...

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Journal of environmental management
Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying ...

Uncovering and Expressing our Purpose.

Creative nursing
Generative artificial intelligence (AI) is currently a source of angst, because of its ability to give us content that sounds uncannily like a real person, and because of concern that people will not stop at using it as a tool to generate and synthes...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Critical care explorations
Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine...

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

Expert opinion on drug safety
INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medic...

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

IEEE journal of biomedical and health informatics
Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the ski...

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

IEEE journal of biomedical and health informatics
The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And ...

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

IEEE journal of biomedical and health informatics
Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable pe...